Statistical Tests for Force Inference in Heterogeneous Environments.

Area of Science:

• Statistical Physics
• Biophysics
• Computational Biology

Background:

• Stochastic trajectories are crucial for understanding molecular dynamics.
• Heterogeneous environments introduce complexities like spurious forces in Langevin equation models.
• Accurate force estimation is vital for analyzing biomolecular interactions and functions.

Purpose of the Study:

• To develop a robust method for detecting and estimating forces in heterogeneous environments using experimental stochastic trajectories.
• To address the challenge of spurious forces arising from spatially varying diffusivity.
• To incorporate Bayesian inference for reliable force estimation, accounting for unknown spurious forces and experimental errors.

Main Methods:

• Utilizing Bayesian inference to reliably infer forces by marginalizing the force posterior over all possible spurious force contributions.
• Employing a Bayes factor statistical test to determine the presence of forces.
• Analytical, numerical, and experimental validation of the developed method.

Main Results:

• A closed-form solution for force estimation, enabling efficient computation and property exploration.
• Demonstrated reliable inference of forces even in the presence of unknown spurious forces and experimental noise.
• Successful application and testing on experimental datasets.

Conclusions:

• The developed Bayesian inference method provides a powerful tool for analyzing forces in complex, heterogeneous systems.
• The method is integrated into TRamWAy, an open-source platform, facilitating automated analysis of biomolecular trajectories.
• This work advances the capabilities for quantitative analysis in biophysics and statistical mechanics.